Fully symmetric kernel quadrature

TL;DR

This paper introduces a method to efficiently compute kernel quadrature weights for large node sets by exploiting symmetry, enabling practical high-dimensional integration with millions of nodes.

Contribution

It presents a novel approach that leverages symmetry to reduce computational complexity in kernel quadrature, allowing exact weight computation for very large node sets.

Findings

Efficient weight computation for up to tens of millions of nodes.

Feasibility demonstrated in high-dimensional settings.

Application to sparse grid quadrature rules.

Abstract

Kernel quadratures and other kernel-based approximation methods typically suffer from prohibitive cubic time and quadratic space complexity in the number of function evaluations. The problem arises because a system of linear equations needs to be solved. In this article we show that the weights of a kernel quadrature rule can be computed efficiently and exactly for up to tens of millions of nodes if the kernel, integration domain, and measure are fully symmetric and the node set is a union of fully symmetric sets. This is based on the observations that in such a setting there are only as many distinct weights as there are fully symmetric sets and that these weights can be solved from a linear system of equations constructed out of row sums of certain submatrices of the full kernel matrix. We present several numerical examples that show feasibility, both for a large number of nodes and in high dimensions, of the developed fully symmetric kernel quadrature rules. Most prominent of the fully symmetric kernel quadrature rules we propose are those that use sparse grids.